{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gQuant - Making Quantitative Analysis Faster\n",
    "\n",
    "## Background\n",
    "By definition, **Quantitative Finance** is the use of mathematical models and large datasets to analyze financial markets and securities, requiring massive computation to extract insight from the data. \n",
    "\n",
    "Many data science toolkits have been developed to help data scientists to manipulate the data. It starts with scalar number computations at the beginning. Later, the development of [Numpy](https://www.numpy.org) library helps to operate the numbers at vectors, and the popular [Pandas](https://pandas.pydata.org) library operates at a dataframe level. Manipulating data at a high level brings productivity gain for data scientists in quantitative finance.\n",
    "\n",
    "However, the amount of collected data is increasing exponentially over time.  Also, more and more machine learning and statistical models are being developed. As a result, data scientists are facing new challenges hard to deal with traditional data science libraries.\n",
    "\n",
    "It is very time-consuming for CPUs to crunch massive amount of data and compute the complicated data science models. Large data set requires distributed computation, which is too complicated for data scientists to adopt.\n",
    "\n",
    "As a consequence, the quantitative workflow has become more complicated than ever. It integrates massive data from different sources, requiring multiple iterations to obtain significative results. \n",
    "\n",
    "**gQuant** has been developed to address all these challenges by organizing dataframes into graphs. It introduces the idea of **dataframe-flow**, which manipulates dataframes at graph level. An **acyclic directed graph** is defined, where the nodes are dataframe processors and the edges are the directions of passing resulting dataframes.\n",
    "\n",
    "With a graph approach, quant's workflow is described at a high level, letting quant analysts address the complicated workflow challenge.\n",
    "\n",
    "It is GPU-accelerated by leveraging [RAPIDS.ai](https://rapids.ai) technology and has **Multi-GPU and Multi-Node support**.\n",
    "\n",
    "We can get orders of magnitude performance boosts compared to CPU. gQuant dataframe-flow is **dataframe agnostic**, and can flow:\n",
    "- Pandas dataframe, computed in the CPU.\n",
    "- cuDF dataframe, computed in the GPU and producing the same result but much faster.\n",
    "- dask_cuDF dataframe, being the computation automatically executed on multiple nodes and multiple GPUs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download example datasets\n",
    "\n",
    "Before getting started, let's download the example datasets if not present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset is already present. No need to re-download it.\n"
     ]
    }
   ],
   "source": [
    "! ((test ! -f './data/stock_price_hist.csv.gz' ||  test ! -f './data/security_master.csv.gz') && \\\n",
    "  cd .. && bash download_data.sh) || echo \"Dataset is already present. No need to re-download it.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The toy example\n",
    "In this notebook, we will use a simple toy example to show how easy it is to accelerate the quant workflow in the GPU.\n",
    "\n",
    "To mimic the end-to-end quantitative analyst task, we are going to backtest a simple mean reversion trading strategy.\n",
    "\n",
    "The workflow includes following steps:\n",
    "\n",
    "1. Load the 5000 end-of-day stocks CSV data into the dataframe.\n",
    "2. Add rate of return feature to the dataframe.\n",
    "3. Clean up the data by removing low volume stocks and extreme rate of returns stocks.\n",
    "4. Compute the slow and fast exponential moving average and compute the trading signal based on it.\n",
    "5. Run backtesting and compute the returns from this strategy for each of the days and stock symbols.\n",
    "6. Run a simple portfolio optimization by averaging the stocks together for each of the trading days.\n",
    "7. Compute the sharpe ratio and cumulative return results.\n",
    "\n",
    "The whole workflow can be organized into a computation graph, which is described in a **yaml** file.\n",
    "\n",
    "Here is snippet of the yaml file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- id:  load_csv_data\n",
      "  type: CsvStockLoader\n",
      "  conf:\n",
      "    path: ./data/stock_price_hist.csv.gz\n",
      "  inputs: []\n",
      "- id: sort\n",
      "  type: SortNode\n",
      "  conf:\n",
      "    keys:\n",
      "      - asset\n",
      "      - datetime\n",
      "  inputs:\n",
      "    -  load_csv_data\n",
      "- id: add_return\n",
      "  type: ReturnFeatureNode\n",
      "  conf: {}\n",
      "  inputs:\n",
      "    - sort\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "!head -n 18 ../task_example/port_trade.yaml\n",
    "print(\"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each node is composed of:\n",
    "- a unique id,\n",
    "- a node type, \n",
    "- configuration parameters\n",
    "- from zero to many input nodes ids.\n",
    "\n",
    "gQuant's `load_taskgraph` and `viz_graph` takes this yaml file, and wires it into a graph.\n",
    "\n",
    "We  use nxpd's `draw` method to visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys; sys.path.insert(0, '..')\n",
    "from gquant.dataframe_flow import TaskGraph\n",
    "\n",
    "task_graph = TaskGraph.load_taskgraph('../task_example/port_trade.yaml')\n",
    "task_graph.draw(show='ipynb')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be observed that the graph above represents the computation steps described at the beginning of this section. \n",
    "\n",
    "## Node implementation\n",
    "gQuant implementation includes some common nodes, useful for quantitative finance. With the help of [Numba](https://numba.pydata.org) library, we have implemented more than 30 technical indicators used in computing trading signals. All of them computed in the GPU.\n",
    "\n",
    "However, gQuant's goal is not to be comprehensive for quant applications. It provides a framework that is easy for anyone to implement his own nodes in the gQuant.\n",
    "\n",
    "Data scientists only need to override two methods in the parent class `Node`:\n",
    "- `columns_setup`\n",
    "- `process`\n",
    "\n",
    "`columns_setup` method  is used to define the required column names and types for both input and output dataframes.\n",
    "\n",
    "`process` method takes input dataframes and computes the output dataframe. \n",
    "\n",
    "In this way, dataframes are strongly typed, and errors can be detected early before the time-consuming computation happens.\n",
    "\n",
    "Here is the code example for implementing `MaxNode`, which is to compute the maximum value for a specified column in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gquant.dataframe_flow import Node\n",
    "\n",
    "class MaxNode(Node):\n",
    "    def columns_setup(self):\n",
    "        \"\"\"\n",
    "            This method is used to define:\n",
    "               - required input dataframes column names and types,\n",
    "               - column names and types of the output dataframe.\n",
    "\n",
    "            In this example, the input dataframe must have an `asset` column of type `int64`.\n",
    "\n",
    "            The output dataframe will consist of two columns:\n",
    "               - The 1st column will be named with the value of the config parameter `@value`,\n",
    "                 and its datatype will be `float64`.\n",
    "               - The 2nd column will be named `asset` and its datatype will be `int64`.\n",
    "        \"\"\"\n",
    "        self.required = {\"asset\": \"int64\"}\n",
    "        self.retention = {self.conf['column']: \"float64\",\n",
    "                          \"asset\": \"int64\"}    \n",
    "    \n",
    "    def process(self, inputs):\n",
    "        \"\"\"\n",
    "            This method is used to calculate the maximum value of the `asset` column from the\n",
    "            input dataframe.\n",
    "\n",
    "            The input and output dataframes structure are defined in the `columns_setup` method.\n",
    "        \"\"\"\n",
    "        input_df = inputs[0]\n",
    "        max_column = self.conf['column']\n",
    "        volume_df = input_df[[max_column, \"asset\"]].groupby([\"asset\"]).max().reset_index()\n",
    "        volume_df.columns = ['asset', max_column]\n",
    "        \n",
    "        return volume_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case that there is no direct dataframe method for a particular logic, a Numba GPU kernel can be used to implement it. Some examples of customized GPU kernels in Numba can be found [here](https://github.com/rapidsai/gQuant/blob/master/notebooks/05_customize_nodes.ipynb).\n",
    "\n",
    "If we use customized GPU kernel functions inside the `process` method to process the dataframe instead of _normal_ dataframe API functions calls,  we need to add `self.delayed_process = True` in the `columns_setup` method to let gQuant handle the dask graph integration problem. If we use  _normal_ dataframe API functions inside the `process` method, nothing needs to be done as `self.delayed_process = False` by default.\n",
    "\n",
    "gQuant automatically handles the complication of including a customized GPU kernel node into the Dask computation graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running a task graph\n",
    "gQuant graph is evaluated by specifying the output nodes and input nodes replacement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings; warnings.simplefilter(\"ignore\")\n",
    "\n",
    "# Define some constants for the data filters.\n",
    "# If using a GPU of 32G memory, you can safely \n",
    "# set the `min_volume` to 5.0\n",
    "min_volume = 400.0\n",
    "min_rate = -10.0\n",
    "max_rate = 10.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id:sort process time:0.144s\n",
      "id:add_return process time:0.401s\n",
      "id:add_indicator process time:0.046s\n",
      "id:volume_mean process time:0.106s\n",
      "id:rename_mean_volume process time:0.002s\n",
      "id:left_merge_mean_volume process time:0.026s\n",
      "id:max_returns process time:0.022s\n",
      "id:rename_max_return process time:0.001s\n",
      "id:left_merge_max_return process time:0.038s\n",
      "id:min_returns process time:0.022s\n",
      "id:rename_min_return process time:0.001s\n",
      "id:left_merge_min_return process time:0.037s\n",
      "id:filter_value process time:0.323s\n",
      "id:drop_columns process time:0.009s\n",
      "id:sort_2 process time:0.049s\n",
      "id:exp_strategy process time:0.936s\n",
      "id:backtest process time:0.038s\n",
      "id:portfolio_opt process time:0.039s\n",
      "id:sharpe_ratio process time:0.001s\n",
      "id:cumlative_return process time:2.063s\n",
      "CPU times: user 5.36 s, sys: 1.09 s, total: 6.45 s\n",
      "Wall time: 6.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "action = \"load\" if os.path.isfile('./.cache/load_csv_data.hdf5') else \"save\"\n",
    "o_gpu = task_graph.run(\n",
    "            outputs=['sharpe_ratio', 'cumlative_return', 'load_csv_data', 'sort_2'],\n",
    "            replace={'filter_value': {\"conf\": [{\"column\": \"volume_mean\", \"min\": min_volume},\n",
    "                                                   {\"column\": \"returns_max\", \"max\": max_rate},\n",
    "                                                   {\"column\": \"returns_min\", \"min\": min_rate}]},\n",
    "                     'load_csv_data': {action: True}}, profile=True)\n",
    "\n",
    "gpu_input_cached = o_gpu[2]  # 'load_csv_data' node output\n",
    "gpu_strategy_cached = o_gpu[3]  # 'sort_2' node output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the example above, `filter_value` node parameters are overridden by the `replace` arguments.\n",
    "\n",
    "`o_gpu` will contain the outputs of four nodes: `sharpe_ratio`, `cumlative_return`, `load_csv_data`, `sort_2`.\n",
    "\n",
    "Similarly, the output from `load_csv_data` and `sort_2` nodes will be cached stored in `gpu_input_cached` and `strategy_cached` variables for later use. \n",
    "\n",
    "`filter_value` node configuration is set up to filter out the stocks that are not suitable for backtesting. It will discard stocks according to the values stored in `min_volume`, `min_rate`, and `max_rate` variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5052 stocks in original dataset.\n",
      "1558 stocks remaining after filtering.\n"
     ]
    }
   ],
   "source": [
    "print(\"{} stocks in original dataset.\".format(len(gpu_input_cached['asset'].unique())))\n",
    "print(\"{} stocks remaining after filtering.\".format(len(gpu_strategy_cached['asset'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[bqplot](https://github.com/bloomberg/bqplot) library is used to visualize the backtesting results in the JupyterLab notebooks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b7479ffb4ae24389b92e15aa48847969",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale(), side='left'), Axis(l…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define the function to format the plots\n",
    "def plot_figures(outputs):\n",
    "    # format the figures\n",
    "    figure_width = '1200px'\n",
    "    figure_height = '400px'\n",
    "    sharpe_number = outputs[0]\n",
    "    cum_return = outputs[1]\n",
    "    cum_return.layout.height = figure_height\n",
    "    cum_return.layout.width = figure_width\n",
    "    cum_return.title = 'P & L %.3f' % (sharpe_number)\n",
    "    return cum_return\n",
    "\n",
    "plot_figures(o_gpu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This toy strategy gets a Sharpe ratio 0.338 without considering the transaction cost. Nice! \n",
    "\n",
    "Next, we are going to compare the performance difference between CPU and GPU. The same computation graph can be used to flow the CPU Pandas dataframe with a few changes:\n",
    "\n",
    "1. the root node need to be changed to load the Pandas dataframe\n",
    "2. a few computation nodes that use Numba GPU kernels need to be changed to use CPU implementations \n",
    "\n",
    "Those nodes that uses compatible dataframe API calls can be leaved as is. We can simply change the Node type in the graph to change the implementations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id:load_csv_data process time:88.344s\n",
      "id:sort process time:5.336s\n",
      "id:add_return process time:20.408s\n",
      "id:add_indicator process time:6.722s\n",
      "id:volume_mean process time:0.347s\n",
      "id:rename_mean_volume process time:0.002s\n",
      "id:left_merge_mean_volume process time:4.962s\n",
      "id:max_returns process time:0.346s\n",
      "id:rename_max_return process time:0.001s\n",
      "id:left_merge_max_return process time:4.598s\n",
      "id:min_returns process time:0.347s\n",
      "id:rename_min_return process time:0.002s\n",
      "id:left_merge_min_return process time:4.709s\n",
      "id:filter_value process time:0.928s\n",
      "id:drop_columns process time:0.068s\n",
      "id:sort_2 process time:1.100s\n",
      "id:exp_strategy process time:11.242s\n",
      "id:backtest process time:0.025s\n",
      "id:portfolio_opt process time:0.300s\n",
      "id:sharpe_ratio process time:0.001s\n",
      "id:cumlative_return process time:0.077s\n",
      "CPU times: user 2min 23s, sys: 6.82 s, total: 2min 30s\n",
      "Wall time: 2min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "o_cpu = task_graph.run(\n",
    "            outputs=['sharpe_ratio', 'cumlative_return', 'load_csv_data', 'sort_2'],\n",
    "            replace={'load_csv_data': {\"type\": \"PandasCsvStockLoader\"},\n",
    "                     'filter_value': {\"conf\": [{\"column\": \"volume_mean\", \"min\": min_volume},\n",
    "                                               {\"column\": \"returns_max\", \"max\": max_rate},\n",
    "                                               {\"column\": \"returns_min\", \"min\": min_rate}]},\n",
    "                     'add_return': {\"type\": \"CpuReturnFeatureNode\"},\n",
    "                     'add_indicator': {\"type\": \"CpuAssetIndicatorNode\"},\n",
    "                     'exp_strategy': {\"type\": \"CpuPortExpMovingAverageStrategyNode\"}}, profile=True)\n",
    "\n",
    "cpu_input_cached = o_cpu[2]  # 'load_csv_data' node output\n",
    "cpu_strategy_cached = o_cpu[3]  # 'sort_2' node output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a7bd2cb99cf4984ace62f15e687d992",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale()), Axis(label='Time', …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_figures(o_cpu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmarks\n",
    "\n",
    "While running this notebook, we have obtained the following results:\n",
    "\n",
    "- 181.00 seconds to run in CPU (Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz).\n",
    "-   9.06 seconds to run in GPU (NVIDIA v100).\n",
    "\n",
    "We get ~20x speed up by using GPU and GPU dataframes, compared to CPU and CPU dataframes.\n",
    "\n",
    "Note, the input nodes load the dataframes from the cache variables to save the disk IO time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distributed computation\n",
    "\n",
    "Running this toy example in a Dask distributed environment is super easy, as gQuant operates at dataframe level.\n",
    "\n",
    "We mostly need to swap cuDF dataframes to **dask_cuDF** dataframes.\n",
    "\n",
    "Let's begin by starting the Dask local cluster environment for distributed computation.\n",
    "\n",
    "Dask provides a web-based dashboard to help to track progress, identify performance issues, and debug failures. To learn more about Dask dashboard, just follow this [link](https://distributed.dask.org/en/latest/web.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:45865</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>4</li>\n",
       "  <li><b>Cores: </b>4</li>\n",
       "  <li><b>Memory: </b>270.39 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:45865' processes=4 threads=4, memory=270.39 GB>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Start the Dask local cluster environment for distrubuted computation\n",
    "from dask_cuda import LocalCUDACluster\n",
    "from dask.distributed import Client\n",
    "\n",
    "cluster = LocalCUDACluster()\n",
    "client = Client(cluster)\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we will split the large dataframe into small pieces to be loaded by different workers in the cluster.\n",
    "\n",
    "Notice this step is need only if the dataset is not split in multiple files yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/Projects/gQuant/notebooks/many-small/0.csv',\n",
       " '/Projects/gQuant/notebooks/many-small/1.csv',\n",
       " '/Projects/gQuant/notebooks/many-small/2.csv',\n",
       " '/Projects/gQuant/notebooks/many-small/3.csv',\n",
       " '/Projects/gQuant/notebooks/many-small/4.csv',\n",
       " '/Projects/gQuant/notebooks/many-small/5.csv',\n",
       " '/Projects/gQuant/notebooks/many-small/6.csv',\n",
       " '/Projects/gQuant/notebooks/many-small/7.csv']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import dask.dataframe as dd\n",
    "import os\n",
    "\n",
    "os.makedirs('many-small', exist_ok=True)\n",
    "dd.from_pandas(cpu_input_cached.set_index('asset'), npartitions=8).reset_index().to_csv('many-small/*.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distributed computation is turned on by changing the root node type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id:load_csv_data process time:0.031s\n",
      "id:volume_mean process time:0.472s\n",
      "id:rename_mean_volume process time:0.012s\n",
      "id:left_merge_mean_volume process time:0.159s\n",
      "id:max_returns process time:0.055s\n",
      "id:rename_max_return process time:0.012s\n",
      "id:left_merge_max_return process time:0.026s\n",
      "id:min_returns process time:0.046s\n",
      "id:rename_min_return process time:0.013s\n",
      "id:left_merge_min_return process time:0.025s\n",
      "id:filter_value process time:0.046s\n",
      "id:backtest process time:0.037s\n",
      "id:portfolio_opt process time:0.420s\n",
      "id:sharpe_ratio process time:8.605s\n",
      "id:cumlative_return process time:12.172s\n",
      "CPU times: user 51.5 s, sys: 1.41 s, total: 52.9 s\n",
      "Wall time: 2min 12s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "o_dask = task_graph.run(\n",
    "            outputs=['sharpe_ratio', 'cumlative_return', 'load_csv_data', 'sort_2'],\n",
    "            replace={'load_csv_data': {\"type\": \"DaskCsvStockLoader\",\n",
    "                                       \"conf\": {\"path\": \"many-small\"}},\n",
    "                     'filter_value': {\"conf\": [{\"column\": \"volume_mean\", \"min\": min_volume},\n",
    "                                               {\"column\": \"returns_max\", \"max\": max_rate},\n",
    "                                               {\"column\": \"returns_min\", \"min\": min_rate}]}}, profile=True)\n",
    "\n",
    "dask_input_cached = o_dask[2]  # 'load_csv_data' node output\n",
    "dask_strategy_cached = o_dask[3]  # 'sort_2' node output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4fa119d3734b4620a80178a2390e53aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale()), Axis(label='Time', …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_figures(o_dask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, it produces the same results. However, the performance is not better than in the other scenarios.\n",
    "\n",
    "Distributed computation only makes sense if we have a very large dataset that cannot be fit into one GPU.\n",
    "\n",
    "In this example, the dataset is small enough to be loaded into a single GPU. The between-GPU communication overhead dominates in the computation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Strategy parameter search\n",
    "Quantitative analysts often need to explore different parameters for their trading strategy.\n",
    "\n",
    "gQuant speeds up this iterative exploration process by using cached dataframes and sub-graphs evaluation.\n",
    "\n",
    "To find the optimal parameters for this toy mean reversion strategy, we only need the dataframe from `sort_2` node, which is cached in the `gpu_strategy_cached` variable.\n",
    "\n",
    "Because the GPU computation is so fast, we can make the parameter exploration interactive in the JupyterLab notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "68552bff4fda44f4b71dfad32f284236",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntRangeSlider(value=(10, 30), continuous_update=False, description='MA:', max=6…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "\n",
    "para_selector = widgets.IntRangeSlider(value=[10, 30],\n",
    "                                       min=3,\n",
    "                                       max=60,\n",
    "                                       step=1,\n",
    "                                       description=\"MA:\",\n",
    "                                       disabled=False,\n",
    "                                       continuous_update=False,\n",
    "                                       orientation='horizontal',\n",
    "                                       readout=True)\n",
    "\n",
    "\n",
    "def para_selection(*stocks):\n",
    "    with out:\n",
    "        para1 = para_selector.value[0]\n",
    "        para2 = para_selector.value[1]\n",
    "        o = task_graph.run(\n",
    "                outputs=['sharpe_ratio', 'cumlative_return'],\n",
    "                replace={'sort_2': {\"load\": gpu_strategy_cached},\n",
    "                         'exp_strategy': {'conf':  {'fast': para1,\n",
    "                                                    'slow': para2}},\n",
    "                         'filter_value': {\"conf\": [{\"column\": \"volume_mean\", \"min\": min_volume},\n",
    "                                                   {\"column\": \"returns_max\", \"max\": max_rate},\n",
    "                                                   {\"column\": \"returns_min\", \"min\": min_rate}]}})\n",
    "\n",
    "        figure_combo = plot_figures(o)\n",
    "        w.children = (w.children[0], figure_combo,)\n",
    "\n",
    "\n",
    "out = widgets.Output(layout={'border': '1px solid black'})\n",
    "para_selector.observe(para_selection, 'value')\n",
    "selectors = widgets.HBox([para_selector])\n",
    "w = widgets.VBox([selectors])\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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